System and method for trellis construction for goup codes

ABSTRACT

A method and system constructs a minimal trellis for decoding a block group code. A generator matrix of the code is obtained and converted into a row-reduced echelon matrix form. Vertices are determined from the row-reduced echelon matrix form at a first time. Further vertices at later times are also determined to obtain a minimal trellis for the block group code C for storing or use in decoding received codes.

CLAIM OF PRIORITY

This patent application claims the benefit of priority, under 35 U.S.C.Section 119(e) to U.S. Provisional Patent Application Ser. No.60/925,047, entitled “System and Method for Trellis Construction forGroup Codes”, filed on Apr. 18, 2007, which is incorporated herein byreference in its entirety.

BACKGROUND

In many communication systems, codes are used to represent data duringtransmission of the data. The codes may have some built in redundancy inorder to ensure communications are properly received. Several things mayinterfere with transmission, and the amount of redundancy built intovarious codes may vary to ensure proper reception in different settings.For instance, noise may be more prevalent in industrial settings, and itmay be more important for the information to be properly received. Insuch a setting, a higher redundancy code may be used for transmission.Signals may also travel on multiple paths, and multiple signals fromdifferent transmissions may be mixed in with the codes beingtransmitted. All these factors may be taken into account when selectingcodes during design of a communication system.

Block group codes are widely used in block-coded modulation schemes. Theconstruction of these codes is important because if sets with more thantwo signals are used for transmission, then group structures (ratherthan field-like structures) match the relevant distance measure of agiven channel.

Since the full potential of block-coded modulation schemes may beachieved by using soft-decision decoding, Trellis based decoding becomesvery attractive for block group codes.

A Trellis is an edge labeled graph. In such a graph, there exists a pairof vertices called the root and toor, at which the Trellis commences andterminates. These are the leftmost and the rightmost verticesrespectively. Each path from the root vertex to the toor vertexrepresents a valid codeword in the code which the Trellis represents. Toget the codewords, the labels of the edges of a path are concatenated.

The Trellis of a code is used for soft-decision decoding of linear orrectangular codes. Trellises permit Maximum-likelihood (ML) decoding. Inthis type of decoding, the codewords of the code which are most likelyto have been transmitted are determined, given the codeword (possiblydistorted by noise) which was received is known. The parameters of theTrellis (like the number of vertices) of a block group code determinethe complexity of decoding. Therefore considerable work has been done onconstructing minimal trellises for block codes.

Various trellis construction techniques are known for codes over finitefields, including a Massey and a BCJR approach. KS (Kalman smoother)construction has been described for a minimal trellis constructionapproach for block codes over finite abelian groups.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a representation of a minimal Massey trellis for a codeaccording to an example embodiment.

FIG. 2 is a pseudo code representation of a method of generating arow-reduced echelon generator matrix according to an example embodiment.

FIG. 3 is a pseudo code representation of a method of generating aminimal trellis using a BCJR technique for a code of length n accordingto an example embodiment.

FIG. 4 is a representation of a minimal BCJR trellis for a codeaccording to an example embodiment.

FIG. 5 is a pseudo code representation of a method of determining thenumber of vertices in a minimal trellis for a code C according to anexample embodiment.

FIG. 6 is a pseudo code representation of a method of determining thenumber of edges in a minimal trellis for code C according to an exampleembodiment.

FIG. 7 is a pseudo code representation of an alternative method ofdetermining the number of edges in a minimal trellis for code Caccording to an example embodiment.

FIG. 8 is a flowchart of a method of generating a Massey trellisaccording to an example embodiment.

FIG. 9 is a block diagram of a system for generating a Massey trellisaccording to an example embodiment.

FIG. 10 is a flowchart of a method of generating a BCJR trellisaccording to an example embodiment.

FIG. 11 is an example embodiment of a computer system for performing themethods and algorithms according to example embodiments.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanyingdrawings that form a part hereof, and in which is shown by way ofillustration specific embodiments which may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the invention, and it is to be understood thatother embodiments may be utilized and that structural, logical andelectrical changes may be made without departing from the scope of thepresent invention. The following description of example embodiments is,therefore, not to be taken in a limited sense, and the scope of thepresent invention is defined by the appended claims.

The functions or algorithms described herein may be implemented insoftware or a combination of software and human implemented proceduresin one embodiment. The software may consist of computer executableinstructions stored on computer readable media such as memory or othertype of storage devices. The term “computer readable media” is also usedto represent any means by which the computer readable instructions maybe received by the computer, such as by different forms of wired orwireless transmissions. Further, such functions correspond to modules,which are software, hardware, firmware or any combination thereof.Multiple functions may be performed in one or more modules as desired,and the embodiments described are merely examples. The software may beexecuted on a digital signal processor, ASIC, microprocessor, or othertype of processor operating on a computer system, such as a personalcomputer, server or other computer system.

A Trellis is an edge labeled graph 100, as shown in FIG. 1. In such agraph, there exists a pair of vertices called the root vertex 110 andtoor vertex 120, at which the Trellis commences and terminates. Theseare the leftmost and the rightmost vertices respectively. Each path fromthe root vertex 110 to the toor 120 represents a valid codeword in thecode which the Trellis 100 represents. To get the codewords, the labelsof the edges of a path are concatenated.

A Massey and BCJR approach for minimal trellis construction for codesover finite abelian groups is described in some embodiments.Constructing a Massey trellis is described given the generator matrix ofthe code. The BCJR approach for trellis construction utilizes a paritycheck matrix of the code. Proof of minimality of the construction anddiscussion of the algorithmic complexities of these techniques is alsooffered. Concrete examples are provided for both approaches.

The description is organized in the following manner. A briefintroduction on block codes over finite abelian groups is firstprovided. The Massey approach for trellis construction and theminimality of the approach is also provided. Given a parity check matrixof a block group code its minimal trellis can be constructed using theBCJR algorithm. The BCJR based trellis construction is then provided.

Let G be a finite abelian group. The subgroups of G^(n) are called nlength group codes. A group code of length n can be seen as a linearcode of length mn over GF(p). Further, a generator matrix Ψ isconstructed for such a code using endomorphisms over

Z_(p). Ψ is a k×n matrix of endomorphisms where ψ_(i,j) represents thei,j'th entry of the matrix such that ψ_(i,j):

Z_(p)→

Z_(p). This generator matrix can be used like the generator matrices ofblock linear codes over fields for tasks such as generating thecodewords, given the information set, etc. This generator matrix canalso be used for Massey construction of the minimal Trellis, just likegenerator matrices for codes over fields.

An (n, k) systematic group code C is a code over G^(n) of order |G|^(k)defined by n−k homomorphisms {φj}, such that 1≦j≦(n−k). The codewords ofC can be written as (x₁, . . . x_(k), φ₁(x₁, . . . , x_(k)), φ₂(X₁, . .. , x_(k)), . . . , φ_(n-k)(x₁, . . . , x_(k))). Here x_(k+j)=φ_(j)(x₁,. . . , x_(k)). By the definition of the homomorphism φ, previousexpression is rewritten as

x _(k+j)=⊕_(i≦l≦k)φ_(j)(e, . . . , x ₁ , . . . e)  (1)

Since φ_(j)(e₁, . . . , x₁, . . . e)εG, φ_(j)(e₁, . . . , x₁, . . . e)can be expressed as ψ_(1,j)(x₁), where ψ_(i,j) are endomorphisms over G.Hence,

x _(k+j=⊕) _(i≦l≦k)ψ_(i,j)(x _(i))  (2)

Therefore for systematic codes, the generator matrix can be written asG=[I|k]

$\begin{matrix}{\Psi = \begin{pmatrix}{\Psi_{1,1},\ldots \mspace{14mu},\Psi_{1,{n - k}}} \\{\Psi_{2,1},\ldots \mspace{14mu},\Psi_{2,{n - k}}} \\\vdots \\{\Psi_{k,1}{,\ldots \mspace{14mu},\Psi_{k,{n - k}}}}\end{pmatrix}} & (3)\end{matrix}$

Codewords are formed as

(x₁, . . . x_(n))=[x₁, . . . , x_(k)]G  (4)

Since any finite abelian group can be expressed as G≡C_(d) ₁

C_(d) ₂

. . . C_(d) _(m) 1, any element of G can be written in terms of the mgenerators, {g₁, . . . , g_(m)}. Any element of G can be written as

x=⊕_(1≦i≦m)xβ,_(i)g_(i)  (5)

Now, consider the endomorphisms ψ_(i,j) themselves, let

ψ(g_(i))=⊕_(1≦j≦m)α_(i,j)g_(j)  (6)

Then, ψ can be written as

$\begin{matrix}{\Psi = \begin{pmatrix}{\alpha_{1,1},\ldots \mspace{14mu},\alpha_{1,m}} \\\vdots \\\vdots \\{\alpha_{m,1},\ldots \mspace{14mu},\alpha_{m,m}}\end{pmatrix}} & (7)\end{matrix}$

Thus, using equation (5) and equation (6), ψ(x) can be written as

$\begin{matrix}{{\psi (x)} = {\otimes_{1 \leq h \leq m}{\{ {\sum\limits_{1 \leq i \leq m}{x_{\beta,h}\alpha_{i,h}\mspace{11mu} {mod}\mspace{11mu} d_{h}}} \} g_{h}}}} & (8)\end{matrix}$

Given a generator matrix of a group code C, a Theorem 1 helps usconstruct the generator matrix of the dual code C^(⊥). This is nothingbut the parity check matrix of C. This parity check matrix is used toconstruct a BCJR trellis for C later in the description.

Massey Construction for Codes Over Finite Abelian Group Codes

Massey construction is a technique for obtaining a minimal trellis forany linear block code over finite fields. The technique is appliedherein for finite group codes.

FIG. 8 is a flowchart illustrating a method 800 of constructing atrellis for decoding block group code C. At 810, a generator matrix ofthe codes is obtained. At 820, the generator matrix is converted into arow-reduced echelon matrix form. Vertices are determined at 830 from therow-reduced echelon matrix form at a first time. Further vertices aredetermined at 840 at later times to obtain a minimal Massey trellis forthe block group code C for storing or use in decoding received codes.

FIG. 9 represents a receiver 910 in one example embodiment. Receiver 910includes a generator matrix module 920 that obtains a generator matrixof codes corresponding to a block group code C. A converter 930 convertsthe generator matrix into a row-reduced echelon matrix form. A vertexmodule 940 determines vertices from the row-reduced echelon matrix format a first time. A further vertex module 950 determines further verticesat later times to obtain a minimal Massey trellis for the block groupcode C for storing or use in decoding received codes. In one embodiment,the receiver 910 also includes a decoder 960 that uses the Masseytrellis to decode received codes.

Consider an example where p=2 and every element q of G can be written interms of the products of the underlying elements over GF(2). Forexample, in a code C={0, x, y, xy}, the elements may be represented interms of C₂×C₂ as follows

0→(0,0),x→(0,1),y→(1,0),xy→(1,1)  (9)

Hence, the 2×2 endomorphisms ψ_(i,j) could be like

$\begin{pmatrix}00 \\11\end{pmatrix}.$

The generator matrix of a (2, 3) code would be of the form

$\begin{matrix}{\Psi = \begin{pmatrix}\begin{pmatrix}00 \\11\end{pmatrix} & \begin{pmatrix}01 \\01\end{pmatrix} & \begin{pmatrix}11 \\01\end{pmatrix} \\\begin{pmatrix}00 \\11\end{pmatrix} & \begin{pmatrix}11 \\11\end{pmatrix} & \begin{pmatrix}00 \\00\end{pmatrix}\end{pmatrix}} & (10)\end{matrix}$

The matrix above could be considered to be a 2k×2n matrix over GF(2) andan algorithm 200 in FIG. 2 may be used to convert it into therow-reduced echleon form which may be used for the Massey construction.

In applying algorithm 200 to the code mentioned above, the generatormatrix of the code is as follows (after the various 2×2 endomorphismsare considered as one large matrix:

$\Psi = \begin{pmatrix}00011110 \\01001111 \\01100110 \\11011011\end{pmatrix}$

Using algorithm 200, a row-reduced echleon matrix is obtained:

$\begin{matrix}{\Psi^{\prime} = \begin{pmatrix}10001010 \\01001111 \\00101001 \\00011110\end{pmatrix}} & (11)\end{matrix}$

ψ′, may be used to construct the Massey trellis for it. Let theresulting minimal trellis be T. The starting position of a row x in ψ′is designated as ↑(x). Further, the columns of ψ′ of weight one arerepresented as (γ₁, γ₂, . . . γ_(k)).

Vertices in T at time instant i (represented as V_(i)) are theequivalence classes represented by (c_(i+1), c_(i+2), . . . c_(n)) where

(c ₁ ,c ₂ , . . . , c _(n))=(a ₁ ,a ₂ , . . . a _(m),0,0,0)Ψ′

where (a₁, a₂, . . . a_(m)) is the set of information symbols of theinput such that

m=max{jε(γ₁,γ₂, . . . γ_(k)):j≦i}

Or

V _(i)≅(c _(i+1) ,c _(i+2) , . . . c _(n))  (12)

For the trellis, beginning the initial vertex φ called the root vertexof T at time 0, the equivalence classes at time 1 are

(0000)Ψ′=(0000000)

(1000)Ψ′=(0001010)

Hence there will be two vertices. The equivalence classes at later timesare shown below. At time 2,

(0000)Ψ′=(000000)

(0100)Ψ′=(001111)

(1000)Ψ′=(001010)

(1100)Ψ′=(000101)

and so on. The resulting trellis 100 obtained is as shown in FIG. 1.Clearly, this is isomorphic to a KS trellis construction.

Proof that Massey Construction Produces the Minimal Trellis

THEOREM III.1. The Massey construction as per algorithm 200 is minimal

Proof: We will prove that the resulting Massey trellis over a group istwo way proper, which is sufficient to prove it is minimal. First weprove the forward proper property.

Let T be a Massey Trellis for a code C. Let γ_(m)=i. Let a_(m) be them'th information symbol. The states in the Massey trellis are theequivalence classes represented by

V _(i)≡(a ₁ , . . . , a _(m),0, . . . , 0)G _(k×(m+1,n))  (13)

Where G_(k×(m+1,n)) represents the k rows and the columns m+1, . . . , nof the generator matrix G. Consider a particular vertex vεV_(i−1) in T.It can be represented as a₁, . . . , a_(m−1), 0, . . . , 0)G_(k×(m+1,n))for some fixed a₁, . . . , a_(m−1). the vertex v can be represented bythe sum Σ_(i−1≦j≦n)Σ_(1≦k≦m−1)c_(j,k)a_(k), where c_(m,n) represents theelement in the m′th column and n'th row of G. For this codeword to reacha vertex in the i'th section of the trellis, another information symbolis to be added and the information set becomes (a₁, . . . , a_(m−1),x).The vertex v′εV_(i+1) corresponding to these information symbols can berepresented asv′=Σ_(i−1≦j≦n)Σ_(1≦k≦m−1)c_(j,k)a_(k)+xΣ_(i+1≦j≦n)c_(m,j). Clearly, fordifferent values of x, the sum will be different and hence the trellisis forward proper.

The reverse case may now be proved. Consider two distinct vertices v andv′εV_(i) represented by (a₁, . . . , a_(m),0, . . . , 0)G_(k×i+1,n) and(a₁′, . . . , a_(m)′,0, . . . , 0)G_(k×i+1,n). Let v″εV_(i+1) such thatcodewords from v and v′ converge at v″. Then, since no additionalinformation symbol is introduced at the i+1'th section, the sums of bothinformation sets must be the same as shown below at the i+1'th sectionof the trellis. This means that (a₁, . . . a_(m), 0, . . . , 0)G_(k×i+2,n)=(a₁′, . . . , a_(m)′, 0, . . . , 0)G_(k×i+2,n). However, thesums are different at the i'th section which means that (a₁, . . . ,a_(m), 0, . . . 0)G_(k×i+1,n)=(a₁′, . . . a_(m)′, 0, . . . ,0)G_(k×i+1,n)

Expanding these two conditions:

${\sum\limits_{{i + 1} \leq j \leq n}{\sum\limits_{1 \leq k \leq m}{c_{j,k}a_{k}^{\prime}}}} \neq {\sum\limits_{{i + 1} \leq j \leq n}{\sum\limits_{1 \leq k \leq n}{c_{j,k}a_{k}}}}$${\sum\limits_{{i + 2} \leq j \leq n}{\sum\limits_{1 \leq k \leq m}{c_{j,k}a_{k}^{\prime}}}} \neq {\sum\limits_{{i + 2} \leq j \leq n}{\sum\limits_{1 \leq k \leq m}{c_{j,k}a_{k}}}}$

The above two equations imply that

${\sum\limits_{1 \leq k \leq m}{c_{i + 1}a_{k}}} \neq {\sum\limits_{1 \leq k \leq m}{c_{i + 1}a_{k}^{\prime}}}$

The LHS and RHS of the above equations are the edge labels from vertex vto v″ and v′ to v″ respectively. Hence, all converging states havedistinct labels, which completes the proof that the trellis is proper inthe reverse case.

BCJR Trellis for Group Codes

An alternative method 1000 in FIG. 10 constructs a trellis for decodingblock group code C. At 1010, a generator matrix of the codes isobtained. At 1020, a parity check matrix is derived from the generatormatrix. At 1030, a root vertex at a time 1 is determined. At 1040,distinct vertices at a time 2 are determined. Syndromes corresponding tovertices at a time 3 and a time 4 are then obtained at 1050, and at atime 5, a toor vertex is determined at 1060, wherein the verticescomprise a BCJR trellis.

An algorithm for constructing the minimal trellis for a linear blockcode over GF(p) is extended for construction of group codes over FiniteAbelian groups. The purpose of any technique for constructing a minimaltrellis is to determine the set of vertices at time i in the trellis(denoted by V_(i)). Let P_(i)−(C) represent the codeword pasts, where Cis a linear block code of length n and 1≦≦n. Let H be the parity checkmatrix of the code C.

According to [1], if cεP_(i)−(C), then the equivalence classes formed bythe syndrome Hc^(T) represents the future equivalences, V_(i). In oneexample, consider a (4, 2) group code over C₂ ² with the generatormatrix (equation 14) and parity check matrix (equation 15). A minimaltrellis is constructed for this code using the BCJR technique.

$\begin{matrix}{G = \begin{pmatrix}\begin{pmatrix}10 \\01\end{pmatrix} & \begin{pmatrix}00 \\00\end{pmatrix} & \begin{pmatrix}11 \\01\end{pmatrix} & \begin{pmatrix}11 \\01\end{pmatrix} \\\begin{pmatrix}00 \\00\end{pmatrix} & \begin{pmatrix}10 \\01\end{pmatrix} & \begin{pmatrix}11 \\01\end{pmatrix} & \begin{pmatrix}01 \\10\end{pmatrix}\end{pmatrix}} & (14) \\{H = \begin{pmatrix}\begin{pmatrix}10 \\11\end{pmatrix} & \begin{pmatrix}10 \\11\end{pmatrix} & \begin{pmatrix}10 \\01\end{pmatrix} & \begin{pmatrix}00 \\00\end{pmatrix} \\\begin{pmatrix}10 \\11\end{pmatrix} & \begin{pmatrix}01 \\10\end{pmatrix} & \begin{pmatrix}00 \\00\end{pmatrix} & \begin{pmatrix}10 \\01\end{pmatrix}\end{pmatrix}} & (15)\end{matrix}$

Instead of the usual code alphabet, the matrices G and H above consistof 2×2 matrices which, are endomorphisms as required by equation (3). ABCJR trellis is constructed over the above code. Looking at C₂ ²={00,01, 10, 11}, it is observed that it is generated by the vectors g₁=01and g₂=10. Or,

<g₁,g₂>=C₂ ²

Hence,

-   -   00=g₁ ⁰g₂ ⁰    -   01=g₁ ¹g₂ ⁰    -   10=g₁ ⁰g₂ ¹    -   11=g₁ ¹g₂ ¹

There is one vertex at time i=1 and is called the root vertex. To seehow many vertices there are, at i=2, the syndromes H₁p₁ ^(T) arecalculated:

${{H_{1}(00)}^{T} = \begin{pmatrix}00 \\00\end{pmatrix}},{{H_{1}(10)}^{T} = \begin{pmatrix}10 \\10\end{pmatrix}}$ ${{H_{1}(01)}^{T} = \begin{pmatrix}11 \\11\end{pmatrix}},{{H_{1}(11)}^{T} = \begin{pmatrix}01 \\01\end{pmatrix}}$

which leads to the conclusion that there are four distinct vertices attime i=2.

Similarly, at i=3, calculate the syndromes) H₃p₃ ^(T). The followingsyndromes are obtained:

${{H_{2}(0000)}^{T} = \begin{pmatrix}00 \\00\end{pmatrix}},{{H_{2}(0001)}^{T} = \begin{pmatrix}11 \\10\end{pmatrix}}$ ${{H_{2}(0010)}^{T} = \begin{pmatrix}10 \\01\end{pmatrix}},{{H_{2}(0011)}^{T} = \begin{pmatrix}01 \\11\end{pmatrix}}$ ${{H_{2}(0100)}^{T} = \begin{pmatrix}11 \\11\end{pmatrix}},{{H_{2}(0101)}^{T} = \begin{pmatrix}00 \\01\end{pmatrix}}$ ${{H_{2}(0110)}^{T} = \begin{pmatrix}01 \\10\end{pmatrix}},{{H_{2}(0111)}^{T} = \begin{pmatrix}10 \\00\end{pmatrix}}$ ${{H_{2}(1000)}^{T} = \begin{pmatrix}10 \\10\end{pmatrix}},{{H_{2}(1001)}^{T} = \begin{pmatrix}01 \\00\end{pmatrix}}$ ${{H_{2}(1010)}^{T} = \begin{pmatrix}00 \\11\end{pmatrix}},{{H_{2}(1011)}^{T} = \begin{pmatrix}11 \\01\end{pmatrix}}$ ${{H_{2}(1100)}^{T} = \begin{pmatrix}01 \\01\end{pmatrix}},{{H_{2}(1101)}^{T} = \begin{pmatrix}10 \\11\end{pmatrix}}$ ${{H_{2}(1110)}^{T} = \begin{pmatrix}11 \\00\end{pmatrix}},{{H_{2}(1111)}^{T} = \begin{pmatrix}00 \\10\end{pmatrix}}$

Clearly, all 16 syndromes are distinct and there are 16 vertices at thisstage of the trellis.

At time i=4, the following syndromes are obtained

${{H_{2}(000000)}^{T} = \begin{pmatrix}00 \\00\end{pmatrix}},{{H_{2}(001010)}^{T} = \begin{pmatrix}00 \\01\end{pmatrix}}$ ${{H_{2}(000111)}^{T} = \begin{pmatrix}00 \\10\end{pmatrix}},{{H_{2}(001101)}^{T} = \begin{pmatrix}00 \\11\end{pmatrix}}$ ${{H_{2}(100010)}^{T} = \begin{pmatrix}00 \\10\end{pmatrix}},{{H_{2}(101000)}^{T} = \begin{pmatrix}00 \\11\end{pmatrix}}$ ${{H_{2}(100101)}^{T} = \begin{pmatrix}00 \\00\end{pmatrix}},{{H_{2}(101111)}^{T} = \begin{pmatrix}00 \\01\end{pmatrix}}$ ${{H_{2}(010010)}^{T} = \begin{pmatrix}00 \\11\end{pmatrix}},{{H_{2}(011001)}^{T} = \begin{pmatrix}00 \\10\end{pmatrix}}$ ${{H_{2}(010100)}^{T} = \begin{pmatrix}00 \\01\end{pmatrix}},{{H_{2}(011110)}^{T} = \begin{pmatrix}00 \\00\end{pmatrix}}$ ${{H_{2}(110001)}^{T} = \begin{pmatrix}00 \\01\end{pmatrix}},{{H_{2}(111011)}^{T} = \begin{pmatrix}00 \\00\end{pmatrix}}$ ${{H_{2}(110110)}^{T} = \begin{pmatrix}00 \\11\end{pmatrix}},{{H_{2}(111100)}^{T} = \begin{pmatrix}00 \\10\end{pmatrix}}$

From the above syndromes, it is seen that there are four vertices atthis stage of the trellis with syndromes

$\begin{pmatrix}00 \\00\end{pmatrix},\begin{pmatrix}00 \\01\end{pmatrix},\begin{pmatrix}00 \\10\end{pmatrix},{{and}\mspace{14mu} {\begin{pmatrix}00 \\11\end{pmatrix}.}}$

At time i=5, p₄ will constitute of codewords from C and hence all thesyndromes will be

$\quad\begin{pmatrix}00 \\00\end{pmatrix}$

(null vector) by definition. This single vertex at this stage of thetrellis is called the toor. FIG. 4 illustrates this minimal trellis 400.

Proof of Minimality of the Proposed BCJR Technique

THEOREM IV.1. The Trellis created by algorithm 300 in FIG. 3 is minimal.

Proof The proof of minimality is similar to the proof of minimality of aBCJR trellis over a finite field. The concepts of ˜_(T) equivalence(denoted as ˜_(T)) and future equivalence (denoted as ˜) are used.

Let c₁, c₂εC, where C is a length n linear block code. For some iε[1,n], if P_(i)(c₁)˜_(T)Pi(c₂), then it always follows thatP_(i)(c₁)˜Pi(c₂). For a minimal trellis, the reverse also holds true,i.e. P_(i)(c₁)˜Pi(c₂) always implies that P_(i)(c₁)˜_(T)Pi(c₂). Givencodewords c, c′εC such that c=(c₁, c₂, . . . , c_(n)) and c′=(c′₁, c′₂,. . . , c′_(n)), we have

$\begin{matrix}{{{c_{1}H_{1}} + {c_{2}H_{2}\mspace{11mu} \ldots} + {c_{n}H_{n}}} = \begin{pmatrix}00 \\00\end{pmatrix}} & (16) \\{{{c_{1}^{\prime}H_{1}} + {c_{2}^{\prime}H_{2}\mspace{11mu} \ldots} + {c_{n}^{\prime}H_{n}}} = \begin{pmatrix}00 \\00\end{pmatrix}} & (17)\end{matrix}$

If c and c′ are future equivalent at time i, they must share a commonfuture after the i'th section of the trellis. Let that common future bex=x_(i+1), . . . x_(n). Then, H (P_(i)(c),x)=H(P_(i)(c′),x), which meansthat

c ₁ H ₁ + . . . +c _(i) H _(i)=−(x _(i+i) H _(i+1) + . . . +x _(n) H_(n))

which in turn is equal to c₁′H₁+ . . . +c_(i)′H_(i). Both the aboveequations imply that c and c′ are T-equivalent at some vertex of thetrellis at section i. Hence, future equivalence implies T-equivalence,completing the proof.

The task of Minimal Trellis construction includes

1) Conversion of whichever matrix is used for the construction to adesired form. This includes converting a generator matrix to a two-wayproper form for the KS method, a row-reduced echleon format for theMassey method etc.2) Obtaining the number of vertices (represented as |V_(i)(T)|) in eachsection of the trellis.3) Determining the edge transitions between states of the trellis(E(T)).It is assumed throughout the section that the computation (groupoperation in one embodiment) takes a unit amount of computing power.

Since any parity check matrix can be used for calculating the syndromesH_(i)C_(i) ^(T), the complexity of matrix conversion is Zero.

To determine the number of vertices in the minimal trellis at any time,all the syndromes are calculated. Algorithm 500 in FIG. 5 shows oneexample method. The complexity of this task is |C|n. The complexity ofdetermining the edge transitions in this minimal trellis whose vertexset V(T) is known by the previous algorithm 300. The complexity is shownin algorithm 600 in FIG. 6. Algorithm 600 is of complexity

Σ_(1≦i≦n) n|C|V _(i+1) |,≈n|C∥V(T)|

The generator matrix for the Massey Trellis may be reduced to the rowreduced echleon form. This is bounded by O(k²n). The task of determiningthe vertex sets and edge transitions is the same as for a BCJR trellisas both these techniques rely on calculating syndromes.

The generator matrix for the KS Trellis is bounded by O(k²n). Let thegroup G from which the code is generated be isomorphic to Z_(q). Thenumber of vertices at the i'th time instant in the KS trellis is q^(ri),where r_(i) is the number of rows active at time i. The algorithm whichkeeps track of how many rows are active at time i in a generator matrixis of order O(kn).

Every vertex in the KS trellis at time i can be written as v₁× . . .×v_(k), where v_(i) belongs to the minimal trellis formed by the i'throw alone. This is because the KS trellis is a product trellis.Similarly, every vertex at time i+1 can be written as v₁′× . . .×v_(k)′. An algorithm 700 in FIG. 7 determines the edge transitions inthis trellis. The complexity of the algorithm 700 is

$\sum\limits_{1 \leq i \leq {n - 1}}{k{V_{i}}{V_{i + 1}}}$

In various embodiments, the BCJR technique of trellis generation may bedone without manipulation of the parity check matrix before being usedfor trellis construction. This is not the case for Massey and KStechniques. This makes the BCJR technique computationally attractivewhen compared to the other techniques. For high rate codes, the paritycheck matrices are sparse and the BCJR technique is more computationallyefficient as it uses a parity check matrix rather than using a generatormatrix.

A block diagram of a computer system that may be used to executeprogramming for performing the above algorithms is shown in FIG. 11. Ageneral computing device in the form of a computer 1110, may include aprocessing unit 1102, memory 1104, removable storage 1112, andnon-removable storage 1114. Memory 1104 may include volatile memory 1106and non-volatile memory 1108. Computer 1110 may include—or have accessto a computing environment that includes—a variety of computer-readablemedia, such as volatile memory 1106 and non-volatile memory 1108,removable storage 1112 and non-removable storage 1114. Computer storageincludes random access memory (RAM), read only memory (ROM), erasableprogrammable read-only memory (EPROM) & electrically erasableprogrammable read-only memory (EEPROM), flash memory or other memorytechnologies, compact disc read-only memory (CD ROM), Digital VersatileDisks (DVD) or other optical disk storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium capable of storing computer-readable instructions. Computer1110 may include or have access to a computing environment that includesinput 1116, output 1118, and a communication connection 1120. Thecomputer may operate in a networked environment using a communicationconnection to connect to one or more remote computers. The remotecomputer may include a personal computer (PC), server, router, networkPC, a peer device or other common network node, or the like. Thecommunication connection may include a Local Area Network (LAN), a WideArea Network (WAN) or other networks.

Computer-readable instructions stored on a computer-readable medium areexecutable by the processing unit 1102 of the computer 1110. A harddrive, CD-ROM, and RAM are some examples of articles including acomputer-readable medium.

The Abstract is provided to comply with 37 C.F.R. §1.72(b) to allow thereader to quickly ascertain the nature and gist of the technicaldisclosure. The Abstract is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

1. A method of constructing a trellis for decoding block group code C,the method comprising: obtaining a generator matrix of the codes;converting the generator matrix into a row-reduced echelon matrix form;determining vertices from the row-reduced echelon matrix form at a firsttime; and determining further vertices at later times to obtain aminimal Massey trellis for the block group code C for storing or use indecoding received codes.
 2. The method of claim 1 wherein the blockgroup codes comprises a finite abelian group.
 3. The method of claim 1wherein the Massey trellis is obtained for a linear block code overfinite fields.
 4. The method of claim 1 wherein the vertices areequivalence classes.
 5. The method of claim 1 and further comprisingusing the Massey trellis to decode received codes.
 6. The method ofclaim 1 wherein the Massey trellis is constructed at a receiver.
 7. Areceiver comprising: means for obtaining a generator matrix of codescorresponding to a block group code C; means for converting thegenerator matrix into a row-reduced echelon matrix form; means fordetermining vertices from the row-reduced echelon matrix form at a firsttime; and means for determining further vertices at later times toobtain a minimal Massey trellis for the block group code C for storingor use in decoding received codes.
 8. The receiver of claim 7 whereinthe block group codes comprises a finite abelian group.
 9. The receiverof claim 7 wherein the Massey trellis is obtained for a linear blockcode over finite fields.
 10. The receiver of claim 7 wherein thevertices are equivalence classes.
 11. The receiver of claim 7 andfurther comprising a module that uses the Massey trellis to decodereceived codes.
 12. A machine readable medium having instructions forexecution by a system for performing a method comprising: obtaining agenerator matrix of codes corresponding to a block group code C;converting the generator matrix into a row-reduced echelon matrix form;determining vertices from the row-reduced echelon matrix form at a firsttime; and determining further vertices at later times to obtain aminimal Massey trellis for the block group code C for storing or use indecoding received codes.
 13. The machine readable medium of claim 11wherein the block group codes comprises a finite abelian group.
 14. Themachine readable medium of claim 11 wherein the Massey trellis isobtained for a linear block code over finite fields.
 15. The machinereadable medium of claim 11 wherein the vertices are equivalenceclasses.
 16. The machine readable medium of claim 11 wherein the methodfurther comprising using the Massey trellis to decode received codes.17. The machine readable medium of claim 11 wherein the Massey trellisis constructed at a receiver.
 18. (canceled)